{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 7. 什么是GARCH模型？ "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 目录\n",
    "1. ARCH\n",
    "2. GARCH"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ARCH(1)模型就是上式没有方差项，\n",
    "\n",
    "$ \\epsilon_t=\\sigma_tw_t$\n",
    "\n",
    "\n",
    "$ \\sigma_t^2=\\alpha_0+\\alpha_1\\epsilon_{t-1}^2$\n",
    "\n",
    "所以ARCH其实是特殊的GARCH。\n",
    "\n",
    "而GARCH模型，简单的来理解就是将ARMA模型应用到残差平方和上。来看个GARCH(1，1)的例子。\n",
    "\n",
    "$ \\epsilon_t=\\sigma_tw_t$\n",
    "\n",
    "$ \\sigma_t^2=\\alpha_0+\\alpha_1\\epsilon_{t-1}^2+\\beta_1\\sigma_{t-1}^2$\n",
    "\n",
    "其中$w_t$~$IID(0,1)$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from scipy import  stats\n",
    "import statsmodels.api as sm  # 统计相关的库\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import tushare as ts\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import arch  # 条件异方差模型相关的库"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-value:    0.006009307564\n"
     ]
    }
   ],
   "source": [
    "data=ts.get_k_data('000001', start='2016-01-01', end='2016-12-31', ktype='D',autype='qfq')\n",
    "data.index = pd.to_datetime(data['date'],format='%Y-%m-%d')\n",
    "\n",
    "data=data['close'].diff()[1:]\n",
    "\n",
    "t = sm.tsa.stattools.adfuller(data)  # ADF检验\n",
    "print \"p-value:   \",t[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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NAAAAAOgQGgEAAADQITQCGKDJyZbxM56XQ+e9JPc+/I1MTrZhlwQAAPC0CI0A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\nJgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x5f5de48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(20,5))\n",
    "ax1=fig.add_subplot(111)\n",
    "fig = sm.graphics.tsa.plot_pacf(data,lags = 20,ax=ax1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\ProgramData\\Anaconda2\\lib\\site-packages\\statsmodels\\base\\model.py:496: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n"
     ]
    }
   ],
   "source": [
    "order = (13,0)\n",
    "model = sm.tsa.ARMA(data,order).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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t2zF53Iqi4rtPHijaqBuywXrnxMCk3FRfO9KHj/56C/bo5yBtQjLEwBCQ7+xQ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LNS3mDyzNfnLpiLe24+G6UFdr1cWVJgx7yqQjy66Tj++4W9aO3z6isMSwvsKHba9IZhY+ar1G1H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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x6c185518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "at = data -  model.fittedvalues\n",
    "at2 = np.square(at)\n",
    "plt.figure(figsize=(10,6))\n",
    "plt.subplot(211)\n",
    "plt.plot(at,label = 'at')\n",
    "plt.legend()\n",
    "plt.subplot(212)\n",
    "plt.plot(at2,label='at^2')\n",
    "plt.legend(loc=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AC</th>\n",
       "      <th>Q</th>\n",
       "      <th>P-value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lag</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>0.014345</td>\n",
       "      <td>0.050625</td>\n",
       "      <td>0.821980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>0.118677</td>\n",
       "      <td>3.529925</td>\n",
       "      <td>0.171193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>-0.011064</td>\n",
       "      <td>3.560289</td>\n",
       "      <td>0.313027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>0.098516</td>\n",
       "      <td>5.977886</td>\n",
       "      <td>0.200806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.0</th>\n",
       "      <td>-0.041359</td>\n",
       "      <td>6.405776</td>\n",
       "      <td>0.268712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6.0</th>\n",
       "      <td>0.195397</td>\n",
       "      <td>15.996641</td>\n",
       "      <td>0.013772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.0</th>\n",
       "      <td>0.164832</td>\n",
       "      <td>22.850651</td>\n",
       "      <td>0.001810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8.0</th>\n",
       "      <td>0.086483</td>\n",
       "      <td>24.745471</td>\n",
       "      <td>0.001717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9.0</th>\n",
       "      <td>-0.030288</td>\n",
       "      <td>24.978876</td>\n",
       "      <td>0.002995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10.0</th>\n",
       "      <td>0.111481</td>\n",
       "      <td>28.154395</td>\n",
       "      <td>0.001705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11.0</th>\n",
       "      <td>0.068774</td>\n",
       "      <td>29.368142</td>\n",
       "      <td>0.001990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12.0</th>\n",
       "      <td>-0.037068</td>\n",
       "      <td>29.722275</td>\n",
       "      <td>0.003074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13.0</th>\n",
       "      <td>0.091084</td>\n",
       "      <td>31.869749</td>\n",
       "      <td>0.002511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14.0</th>\n",
       "      <td>0.100179</td>\n",
       "      <td>34.478821</td>\n",
       "      <td>0.001755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15.0</th>\n",
       "      <td>-0.030408</td>\n",
       "      <td>34.720258</td>\n",
       "      <td>0.002695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16.0</th>\n",
       "      <td>0.114265</td>\n",
       "      <td>38.144586</td>\n",
       "      <td>0.001443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17.0</th>\n",
       "      <td>0.079768</td>\n",
       "      <td>39.820761</td>\n",
       "      <td>0.001372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18.0</th>\n",
       "      <td>0.014486</td>\n",
       "      <td>39.876289</td>\n",
       "      <td>0.002170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19.0</th>\n",
       "      <td>-0.027723</td>\n",
       "      <td>40.080557</td>\n",
       "      <td>0.003194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20.0</th>\n",
       "      <td>0.082916</td>\n",
       "      <td>41.916019</td>\n",
       "      <td>0.002836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21.0</th>\n",
       "      <td>0.042725</td>\n",
       "      <td>42.405544</td>\n",
       "      <td>0.003740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22.0</th>\n",
       "      <td>0.021755</td>\n",
       "      <td>42.533045</td>\n",
       "      <td>0.005384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23.0</th>\n",
       "      <td>0.132125</td>\n",
       "      <td>47.257158</td>\n",
       "      <td>0.002080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24.0</th>\n",
       "      <td>0.189225</td>\n",
       "      <td>56.991030</td>\n",
       "      <td>0.000168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25.0</th>\n",
       "      <td>-0.028426</td>\n",
       "      <td>57.211696</td>\n",
       "      <td>0.000250</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            AC          Q   P-value\n",
       "lag                                \n",
       "1.0   0.014345   0.050625  0.821980\n",
       "2.0   0.118677   3.529925  0.171193\n",
       "3.0  -0.011064   3.560289  0.313027\n",
       "4.0   0.098516   5.977886  0.200806\n",
       "5.0  -0.041359   6.405776  0.268712\n",
       "6.0   0.195397  15.996641  0.013772\n",
       "7.0   0.164832  22.850651  0.001810\n",
       "8.0   0.086483  24.745471  0.001717\n",
       "9.0  -0.030288  24.978876  0.002995\n",
       "10.0  0.111481  28.154395  0.001705\n",
       "11.0  0.068774  29.368142  0.001990\n",
       "12.0 -0.037068  29.722275  0.003074\n",
       "13.0  0.091084  31.869749  0.002511\n",
       "14.0  0.100179  34.478821  0.001755\n",
       "15.0 -0.030408  34.720258  0.002695\n",
       "16.0  0.114265  38.144586  0.001443\n",
       "17.0  0.079768  39.820761  0.001372\n",
       "18.0  0.014486  39.876289  0.002170\n",
       "19.0 -0.027723  40.080557  0.003194\n",
       "20.0  0.082916  41.916019  0.002836\n",
       "21.0  0.042725  42.405544  0.003740\n",
       "22.0  0.021755  42.533045  0.005384\n",
       "23.0  0.132125  47.257158  0.002080\n",
       "24.0  0.189225  56.991030  0.000168\n",
       "25.0 -0.028426  57.211696  0.000250"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m = 25 # 我们检验25个自相关系数\n",
    "acf,q,p = sm.tsa.acf(at2,nlags=m,qstat=True)  ## 计算自相关系数 及p-value\n",
    "out = np.c_[range(1,26), acf[1:], q, p]\n",
    "output=pd.DataFrame(out, columns=['lag', \"AC\", \"Q\", \"P-value\"])\n",
    "output = output.set_index('lag')\n",
    "output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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tafDMzB4V+ubQTtZRIQBYXobDmEyszLt/975FH3Jx3JfL5Lgvl9u3nLcoNfB0\ns2c727nvcO4/9ES2rj93LMOCON5sgDu15oLc/cDXxvKenCyIWczPrO8rdM1ChozBkuCoEAAsf104\n0u8of3etWFHZvuW8vGr7Rdm+5Txh0Jh1pTddV4YT+r5C15hun2XDUaGn68IRGQAYpS4c6XeUHxam\nK73pZoOYPQePZPLYdFaNKYjxfYWuEQixrMweFdJduxtd6gFg1LoQxnTlyyV0XRcC3KRbQYzvK3SJ\nQAiWqa4ckQGAUepCGNOlL5fQZV0IcGcJYrrJiIbxEgjBMtWVIzJ0kz++sDA+K93TlTDGl0s4tS4E\nuHSXEQ3jJxCCZapLR2ToFn98YWF8VrpLGANLQ1cCXLrJiIbxc5YxWKacxYD5dOEMPbAU+KwAPHvO\n/MZ8nLFx/PQQgmXKERnmYzghLIzPCgCcOUY0jJ9ACJYxXeo5EX98YWF8VgDgzDHH1PgJhAB6xh9f\nWBifFQA4c4xoGD+BEEDP+OMLC+OzAgBnlhEN4yUQAughf3xhYXxWAIDlylnGAKAjpqdbJte/MEe3\nfHfufuBrmZ5u4y4JAIBlSg8hAOiA6emWd37s3hy54geSiZV59+/elxduXJO3XXO5IUoAAIycHkIA\n0AE79x3OnoNHkpWrkpo5s9Weg0eyc9/hcZcGAMAyJBACgA64/9ATmRw6vXmSTB6bzv2HnhhTRQAA\nLGcCIQDogK3rz82qlcf/WV61ckW2rj93TBUBALCcCYQAoAO2bV6XF25ck7NXrkglOXvlirxw45ps\n27xu3KUBALAMmVQaADpgxYrK2665PDv3Hc79h57I1vXnZtvmdSaUBgDgjBAIAUBHrFhR2b7lvGzf\nct64SwEAYJkzZAwAAACgZwRCAAAAAD1jyNgy8rIXrB93CQAAAMASoIcQAAAAQM8IhAAAAAB6RiAE\nAAAA0DMCIQAAAICeEQgBAAAA9IxACAAAAKBnBEIAAAAAPSMQAgAAAOgZgRAAAABAzwiEAAAAAHpG\nIAQAAADQMwIhAAAAgJ6p1tp4Nlz1SJIHxrLx0Ts/yaPjLgJOQhul67RRuk4bpeu0UbpOG6XrllMb\n3dJa23CqlcYWCC0nVXVna23HuOuA+WijdJ02Stdpo3SdNkrXaaN0XR/bqCFjAAAAAD0jEAIAAADo\nGYHQaLx33AXAKWijdJ02Stdpo3SdNkrXaaN0Xe/aqDmEAAAAAHpGDyEAAACAnhEIPQtVdXVV7a6q\nPVX11nHKbEhQAAADjklEQVTXAydSVfdX1eeramdV3TnueqCqbq6qg1X1haFlz6uq/15V9w1+nzfO\nGum3edroO6rqwcG+dGdVvXKcNdJvVbW5qv5nVd1TVbuq6scHy+1L6YSTtFH7Ujqhqp5TVf+7qj47\naKM/PVjeq/2oIWOnqaomknwxyfcm2Z/kjiTXtdbuGWthMEdV3Z9kR2vt0XHXAklSVX8hyZEkH2yt\nfdtg2f+X5LHW2s8OAvbzWms3jrNO+mueNvqOJEdaa/96nLVBklTV85M8v7V2d1U9N8ldSX4gyQ/H\nvpQOOEkb/aHYl9IBVVVJzm2tHamqs5L8fpIfT/Kq9Gg/qofQ6Xtpkj2ttb2ttckkH05y7ZhrAui8\n1tonkzw2Z/G1SX5pcPmXMvNPI4zFPG0UOqO19lBr7e7B5W8kuTfJhbEvpSNO0kahE9qMI4OrZw1+\nWnq2HxUInb4Lk+wbur4/dnJ0U0vyP6rqrqq6YdzFwDwuaK09NLj81SQXjLMYmMePVdXnBkPKlnUX\ncpaOqtqa5CVJPhP7UjpoThtN7EvpiKqaqKqdSQ4m+e+ttd7tRwVCsPy9vLW2Lck1Sd48GAoBndVm\nxjIbz0zX/GKSb02yLclDSd413nIgqao1SX41yT9orT0+fJt9KV1wgjZqX0pntNamBt+TLkry0qr6\ntjm3L/v9qEDo9D2YZPPQ9YsGy6BTWmsPDn4fTPLfMjPcEbrm4cF8A7PzDhwccz1wnNbaw4N/HKeT\n/MfYlzJmgzkvfjXJf26tfXSw2L6UzjhRG7UvpYtaa4eT/M8kV6dn+1GB0Om7I8mlVXVJVa1K8uok\nt465JjhOVZ07mMgvVXVuku9L8oWT3wvG4tYkrx9cfn2SXx9jLfA0s/8cDvzN2JcyRoPJUN+f5N7W\n2s8N3WRfSifM10btS+mKqtpQVesGl1dn5mRRf5Se7UedZexZGJwm8d8mmUhyc2vtX4y5JDhOVX1r\nZnoFJcnKJL+snTJuVfWhJFclOT/Jw0l+KsmvJflIkouTPJDkh1prJvVlLOZpo1dlZohDS3J/kh8Z\nmmMAFlVVvTzJ/0ry+STTg8Vvy8wcLfaljN1J2uh1sS+lA6rq2zMzafREZjrKfKS19s+qan16tB8V\nCAEAAAD0jCFjAAAAAD0jEAIAAADoGYEQAAAAQM8IhAAAAAB6RiAEAAAA0DMCIQAAAICeEQgBAAAA\n9IxACAAAAKBn/g89lYjyx3AhWQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x6bbacf98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(20,5))\n",
    "ax1=fig.add_subplot(111)\n",
    "fig = sm.graphics.tsa.plot_pacf(at2,lags = 30,ax=ax1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration:      1,   Func. Count:     24,   Neg. LLF: -237.333813474\n",
      "Iteration:      2,   Func. Count:     52,   Neg. LLF: -237.40402314\n",
      "Iteration:      3,   Func. Count:     80,   Neg. LLF: -238.726791833\n",
      "Iteration:      4,   Func. Count:    107,   Neg. LLF: -239.213597268\n",
      "Iteration:      5,   Func. Count:    133,   Neg. LLF: -239.727352484\n",
      "Iteration:      6,   Func. Count:    158,   Neg. LLF: -241.907188423\n",
      "Iteration:      7,   Func. Count:    183,   Neg. LLF: -245.362215313\n",
      "Iteration:      8,   Func. Count:    209,   Neg. LLF: -246.478708982\n",
      "Iteration:      9,   Func. Count:    236,   Neg. LLF: -246.729412765\n",
      "Iteration:     10,   Func. Count:    262,   Neg. LLF: -246.848960921\n",
      "Iteration:     11,   Func. Count:    288,   Neg. LLF: -247.131319672\n",
      "Iteration:     12,   Func. Count:    313,   Neg. LLF: -248.003884498\n",
      "Iteration:     13,   Func. Count:    338,   Neg. LLF: -248.531255524\n",
      "Iteration:     14,   Func. Count:    364,   Neg. LLF: -248.664429881\n",
      "Iteration:     15,   Func. Count:    390,   Neg. LLF: -248.82346318\n",
      "Iteration:     16,   Func. Count:    416,   Neg. LLF: -248.894270952\n",
      "Iteration:     17,   Func. Count:    442,   Neg. LLF: -248.989449051\n",
      "Iteration:     18,   Func. Count:    468,   Neg. LLF: -249.233211667\n",
      "Iteration:     19,   Func. Count:    493,   Neg. LLF: -250.008390823\n",
      "Iteration:     20,   Func. Count:    519,   Neg. LLF: -250.046338474\n",
      "Iteration:     21,   Func. Count:    544,   Neg. LLF: -250.29755998\n",
      "Iteration:     22,   Func. Count:    568,   Neg. LLF: -251.122013769\n",
      "Iteration:     23,   Func. Count:    594,   Neg. LLF: -251.167736594\n",
      "Iteration:     24,   Func. Count:    619,   Neg. LLF: -252.172862587\n",
      "Iteration:     25,   Func. Count:    645,   Neg. LLF: -252.276435452\n",
      "Iteration:     26,   Func. Count:    671,   Neg. LLF: -252.28273888\n",
      "Iteration:     27,   Func. Count:    696,   Neg. LLF: -252.343338268\n",
      "Iteration:     28,   Func. Count:    721,   Neg. LLF: -252.365169623\n",
      "Iteration:     29,   Func. Count:    745,   Neg. LLF: -252.408371475\n",
      "Iteration:     30,   Func. Count:    769,   Neg. LLF: -252.411918522\n",
      "Iteration:     31,   Func. Count:    793,   Neg. LLF: -252.412777642\n",
      "Iteration:     32,   Func. Count:    817,   Neg. LLF: -252.412958764\n",
      "Iteration:     33,   Func. Count:    841,   Neg. LLF: -252.412996346\n",
      "Iteration:     34,   Func. Count:    865,   Neg. LLF: -252.413009667\n",
      "Iteration:     35,   Func. Count:    889,   Neg. LLF: -252.413011481\n",
      "Optimization terminated successfully.    (Exit mode 0)\n",
      "            Current function value: -252.413012001\n",
      "            Iterations: 35\n",
      "            Function evaluations: 890\n",
      "            Gradient evaluations: 35\n"
     ]
    }
   ],
   "source": [
    "train = data[:-10]\n",
    "test = data[-10:]\n",
    "am = arch.arch_model(train,mean='AR',lags=13,vol='ARCH',p=7) \n",
    "res = am.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>AR - ARCH Model Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>        <td>close</td>       <th>  R-squared:         </th>  <td>   0.150</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Mean Model:</th>            <td>AR</td>         <th>  Adj. R-squared:    </th>  <td>   0.096</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Vol Model:</th>            <td>ARCH</td>        <th>  Log-Likelihood:    </th> <td>   252.413</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Distribution:</th>        <td>Normal</td>       <th>  AIC:               </th> <td>  -460.826</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>        <td>Maximum Likelihood</td> <th>  BIC:               </th> <td>  -386.166</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                        <td></td>          <th>  No. Observations:  </th>     <td>220</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>           <td>Sat, Jun 24 2017</td>  <th>  Df Residuals:      </th>     <td>198</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>               <td>12:00:52</td>      <th>  Df Model:          </th>     <td>22</td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Mean Model</caption>\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>      <th>std err</th>       <th>t</th>       <th>P>|t|</th>      <th>95.0% Conf. Int.</th>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Const</th>     <td>1.4927e-03</td>  <td>1.496e-02</td>  <td>9.975e-02</td> <td>    0.921</td> <td>[-2.784e-02,3.082e-02]</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>close[1]</th>  <td>   -0.0850</td>  <td>    0.123</td>  <td>   -0.690</td> <td>    0.490</td>    <td>[ -0.326,  0.156]</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>close[2]</th>  <td>    0.0340</td>  <td>    0.131</td>  <td>    0.260</td> <td>    0.795</td>    <td>[ -0.222,  0.290]</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>close[3]</th>  <td>-4.5421e-04</td> <td>6.748e-02</td> <td>-6.732e-03</td> <td>    0.995</td>    <td>[ -0.133,  0.132]</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>close[4]</th>  <td>   -0.0406</td>  <td>    0.140</td>  <td>   -0.291</td> <td>    0.771</td>    <td>[ -0.314,  0.233]</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>close[5]</th>  <td>   -0.1139</td>  <td>    0.199</td>  <td>   -0.572</td> <td>    0.567</td>    <td>[ -0.504,  0.276]</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>close[6]</th>  <td>   -0.0561</td>  <td>    0.296</td>  <td>   -0.190</td> <td>    0.850</td>    <td>[ -0.636,  0.524]</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>close[7]</th>  <td>-8.9847e-03</td> <td>    0.521</td> <td>-1.724e-02</td> <td>    0.986</td>    <td>[ -1.031,  1.013]</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>close[8]</th>  <td>    0.0203</td>  <td>8.903e-02</td>  <td>    0.228</td> <td>    0.819</td>    <td>[ -0.154,  0.195]</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>close[9]</th>  <td>    0.1843</td>  <td>    0.279</td>  <td>    0.661</td> <td>    0.509</td>    <td>[ -0.362,  0.731]</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>close[10]</th> <td>    0.0917</td>  <td>    0.107</td>  <td>    0.861</td> <td>    0.389</td>    <td>[ -0.117,  0.301]</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>close[11]</th> <td>    0.0745</td>  <td>    0.200</td>  <td>    0.373</td> <td>    0.709</td>    <td>[ -0.317,  0.466]</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>close[12]</th> <td>   -0.0221</td>  <td>    0.136</td>  <td>   -0.162</td> <td>    0.871</td>    <td>[ -0.289,  0.245]</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>close[13]</th> <td>    0.2135</td>  <td>    0.159</td>  <td>    1.340</td> <td>    0.180</td>  <td>[-9.885e-02,  0.526]</td> \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Volatility Model</caption>\n",
       "<tr>\n",
       "      <td></td>        <th>coef</th>     <th>std err</th>      <th>t</th>       <th>P>|t|</th>      <th>95.0% Conf. Int.</th>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>omega</th>    <td>2.9941e-03</td> <td>4.416e-03</td> <td>    0.678</td> <td>    0.498</td> <td>[-5.662e-03,1.165e-02]</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>alpha[1]</th>   <td>0.0000</td>   <td>    0.167</td>   <td>0.000</td>   <td>    1.000</td>    <td>[ -0.328,  0.328]</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>alpha[2]</th> <td>    0.0445</td> <td>6.753e-02</td> <td>    0.659</td> <td>    0.510</td>  <td>[-8.783e-02,  0.177]</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>alpha[3]</th> <td>1.3422e-10</td> <td>    0.404</td> <td>3.319e-10</td> <td>    1.000</td>    <td>[ -0.793,  0.793]</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>alpha[4]</th>   <td>0.0000</td>   <td>    1.217</td>   <td>0.000</td>   <td>    1.000</td>    <td>[ -2.385,  2.385]</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>alpha[5]</th>   <td>0.0000</td>   <td>    0.510</td>   <td>0.000</td>   <td>    1.000</td>    <td>[ -1.000,  1.000]</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>alpha[6]</th> <td>    0.0173</td> <td>    0.674</td> <td>2.560e-02</td> <td>    0.980</td>    <td>[ -1.305,  1.339]</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>alpha[7]</th> <td>    0.5781</td> <td>    1.489</td> <td>    0.388</td> <td>    0.698</td>    <td>[ -2.340,  3.496]</td>  \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                           AR - ARCH Model Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                  close   R-squared:                       0.150\n",
       "Mean Model:                        AR   Adj. R-squared:                  0.096\n",
       "Vol Model:                       ARCH   Log-Likelihood:                252.413\n",
       "Distribution:                  Normal   AIC:                          -460.826\n",
       "Method:            Maximum Likelihood   BIC:                          -386.166\n",
       "                                        No. Observations:                  220\n",
       "Date:                Sat, Jun 24 2017   Df Residuals:                      198\n",
       "Time:                        12:00:52   Df Model:                           22\n",
       "                                  Mean Model                                  \n",
       "==============================================================================\n",
       "                  coef    std err          t      P>|t|       95.0% Conf. Int.\n",
       "------------------------------------------------------------------------------\n",
       "Const       1.4927e-03  1.496e-02  9.975e-02      0.921 [-2.784e-02,3.082e-02]\n",
       "close[1]       -0.0850      0.123     -0.690      0.490      [ -0.326,  0.156]\n",
       "close[2]        0.0340      0.131      0.260      0.795      [ -0.222,  0.290]\n",
       "close[3]   -4.5421e-04  6.748e-02 -6.732e-03      0.995      [ -0.133,  0.132]\n",
       "close[4]       -0.0406      0.140     -0.291      0.771      [ -0.314,  0.233]\n",
       "close[5]       -0.1139      0.199     -0.572      0.567      [ -0.504,  0.276]\n",
       "close[6]       -0.0561      0.296     -0.190      0.850      [ -0.636,  0.524]\n",
       "close[7]   -8.9847e-03      0.521 -1.724e-02      0.986      [ -1.031,  1.013]\n",
       "close[8]        0.0203  8.903e-02      0.228      0.819      [ -0.154,  0.195]\n",
       "close[9]        0.1843      0.279      0.661      0.509      [ -0.362,  0.731]\n",
       "close[10]       0.0917      0.107      0.861      0.389      [ -0.117,  0.301]\n",
       "close[11]       0.0745      0.200      0.373      0.709      [ -0.317,  0.466]\n",
       "close[12]      -0.0221      0.136     -0.162      0.871      [ -0.289,  0.245]\n",
       "close[13]       0.2135      0.159      1.340      0.180   [-9.885e-02,  0.526]\n",
       "                               Volatility Model                              \n",
       "=============================================================================\n",
       "                 coef    std err          t      P>|t|       95.0% Conf. Int.\n",
       "-----------------------------------------------------------------------------\n",
       "omega      2.9941e-03  4.416e-03      0.678      0.498 [-5.662e-03,1.165e-02]\n",
       "alpha[1]       0.0000      0.167      0.000      1.000      [ -0.328,  0.328]\n",
       "alpha[2]       0.0445  6.753e-02      0.659      0.510   [-8.783e-02,  0.177]\n",
       "alpha[3]   1.3422e-10      0.404  3.319e-10      1.000      [ -0.793,  0.793]\n",
       "alpha[4]       0.0000      1.217      0.000      1.000      [ -2.385,  2.385]\n",
       "alpha[5]       0.0000      0.510      0.000      1.000      [ -1.000,  1.000]\n",
       "alpha[6]       0.0173      0.674  2.560e-02      0.980      [ -1.305,  1.339]\n",
       "alpha[7]       0.5781      1.489      0.388      0.698      [ -2.340,  3.496]\n",
       "=============================================================================\n",
       "\n",
       "Covariance estimator: robust\n",
       "\"\"\""
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Const        1.492690e-03\n",
       "close[1]    -8.497512e-02\n",
       "close[2]     3.395028e-02\n",
       "close[3]    -4.542113e-04\n",
       "close[4]    -4.056921e-02\n",
       "close[5]    -1.138923e-01\n",
       "close[6]    -5.611123e-02\n",
       "close[7]    -8.984683e-03\n",
       "close[8]     2.034156e-02\n",
       "close[9]     1.843336e-01\n",
       "close[10]    9.172263e-02\n",
       "close[11]    7.449631e-02\n",
       "close[12]   -2.210782e-02\n",
       "close[13]    2.135335e-01\n",
       "omega        2.994095e-03\n",
       "alpha[1]     0.000000e+00\n",
       "alpha[2]     4.451640e-02\n",
       "alpha[3]     1.342153e-10\n",
       "alpha[4]     0.000000e+00\n",
       "alpha[5]     0.000000e+00\n",
       "alpha[6]     1.726823e-02\n",
       "alpha[7]     5.781235e-01\n",
       "Name: params, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res.params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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4jCESGecRsjxs0NxisUwSrHiMJWFLQ4ViHFY8LBbLJMGKxxgSSceNiIeNeVgslsmBFY+x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\n/5sFWI4eRrNEAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x80040be0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res.hedgehog_plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "233"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train)\n",
    "# print train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration:      5,   Func. Count:    132,   Neg. LLF: -235.533186098\n",
      "Iteration:     10,   Func. Count:    261,   Neg. LLF: -243.173427506\n",
      "Iteration:     15,   Func. Count:    390,   Neg. LLF: -245.420475321\n",
      "Iteration:     20,   Func. Count:    518,   Neg. LLF: -246.607871516\n",
      "Iteration:     25,   Func. Count:    643,   Neg. LLF: -249.174290765\n",
      "Iteration:     30,   Func. Count:    764,   Neg. LLF: -249.410030623\n",
      "Optimization terminated successfully.    (Exit mode 0)\n",
      "            Current function value: -249.410065573\n",
      "            Iterations: 32\n",
      "            Function evaluations: 813\n",
      "            Gradient evaluations: 32\n",
      "h.1    0.062172\n",
      "Name: 2016-12-16 00:00:00, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "res = am.fit(last_obs = '2016-12-13', update_freq=5)\n",
    "forecasts = res.forecast()\n",
    "initial_sigma = np.sqrt(forecasts.variance.iloc[-1])\n",
    "print initial_sigma"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       data    errors  volatility\n",
      "0 -0.117839 -0.108352    0.055003\n",
      "1  0.004387 -0.023033    0.060510\n",
      "2 -0.153155 -0.130864    0.117530\n",
      "3  0.006810  0.006785    0.053571\n",
      "4  0.033508  0.001612    0.072745\n",
      "5 -0.066154 -0.075206    0.083653\n",
      "6 -0.092489 -0.115089    0.092654\n",
      "7  0.133107  0.117837    0.102224\n",
      "8 -0.126163 -0.163520    0.063645\n",
      "9  0.107995  0.105644    0.119326\n",
      "date\n",
      "2016-12-19   -0.05\n",
      "2016-12-20   -0.09\n",
      "2016-12-21    0.05\n",
      "2016-12-22   -0.02\n",
      "2016-12-23   -0.06\n",
      "2016-12-26    0.04\n",
      "2016-12-27   -0.04\n",
      "2016-12-28   -0.02\n",
      "2016-12-29    0.02\n",
      "2016-12-30    0.02\n",
      "Name: close, dtype: float64\n",
      "                data    errors  volatility  test_returns\n",
      "date                                                    \n",
      "2016-12-19 -0.117839 -0.108352    0.055003         -0.05\n",
      "2016-12-20  0.004387 -0.023033    0.060510         -0.09\n",
      "2016-12-21 -0.153155 -0.130864    0.117530          0.05\n",
      "2016-12-22  0.006810  0.006785    0.053571         -0.02\n",
      "2016-12-23  0.033508  0.001612    0.072745         -0.06\n",
      "2016-12-26 -0.066154 -0.075206    0.083653          0.04\n",
      "2016-12-27 -0.092489 -0.115089    0.092654         -0.04\n",
      "2016-12-28  0.133107  0.117837    0.102224         -0.02\n",
      "2016-12-29 -0.126163 -0.163520    0.063645          0.02\n",
      "2016-12-30  0.107995  0.105644    0.119326          0.02\n"
     ]
    }
   ],
   "source": [
    "data_simulate = am.simulate(res.params, 10)\n",
    "print data_simulate\n",
    "print test\n",
    "data_simulate.index = test.index\n",
    "\n",
    "model_simulate = pd.concat([data_simulate,test], axis=1)\n",
    "model_simulate.rename(columns={'close': 'test_returns'}, inplace=True)\n",
    "print model_simulate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "....................                 h.1       h.2       h.3\n",
      "2016-11-21  0.004188  0.004543  0.003063\n",
      "2016-11-22  0.004409  0.003784  0.002633\n",
      "2016-11-23  0.003788  0.002917  0.002557\n",
      "2016-11-24  0.002837  0.002330  0.003395\n",
      "2016-11-25  0.002480  0.004677  0.005781\n",
      "2016-11-28  0.004713  0.005517  0.012883\n",
      "2016-11-29  0.005286  0.012840  0.008346\n",
      "2016-11-30  0.012798  0.008056  0.003896\n",
      "2016-12-01  0.007608  0.003110  0.013394\n",
      "2016-12-02  0.003116  0.012727  0.003215\n",
      "2016-12-05  0.012885  0.003893  0.003568\n",
      "2016-12-06  0.003739  0.002645  0.004953\n",
      "2016-12-07  0.002636  0.004696  0.004329\n",
      "2016-12-08  0.004464  0.004330  0.004466\n",
      "2016-12-09  0.004365  0.004618  0.011191\n",
      "2016-12-12  0.004326  0.013453  0.002545\n",
      "2016-12-13  0.012995  0.002704  0.003397\n",
      "2016-12-14  0.002718  0.002977  0.003158\n",
      "2016-12-15  0.003369  0.005520  0.009061\n",
      "2016-12-16  0.005302  0.008707  0.018891\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "\n",
    "index = data.index\n",
    "start_loc = 0\n",
    "end_loc = np.where(index >= '2016-11-22')[0].min()\n",
    "forecasts = {}\n",
    "for i in range(20):\n",
    "    sys.stdout.write('.')\n",
    "    sys.stdout.flush()\n",
    "    res = am.fit(first_obs=i, last_obs=i+end_loc, disp='off')\n",
    "    temp = res.forecast(horizon=3).variance\n",
    "    fcast = temp.iloc[i+end_loc-1]\n",
    "    forecasts[fcast.name] = fcast\n",
    "\n",
    "print(pd.DataFrame(forecasts).T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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EfELAr+vxW68rY/UYDAYPMMJfBvQkLAA+wenVdUf8xuoxGAylwgh/GYjErWkXgay1esBE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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x802e7198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "fig, ax = plt.subplots(1,1)\n",
    "(res.conditional_volatility['2016'] ** 2.0).plot(ax=ax, title='Conditional Variance')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 h.1\n",
      "date                \n",
      "2016-12-16  0.005302\n"
     ]
    }
   ],
   "source": [
    "pre = res.forecast()\n",
    "print pre.variance.iloc[-1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration:      1,   Func. Count:     24,   Neg. LLF: -237.333813474\n",
      "Iteration:      2,   Func. Count:     52,   Neg. LLF: -237.40402314\n",
      "Iteration:      3,   Func. Count:     80,   Neg. LLF: -238.726791833\n",
      "Iteration:      4,   Func. Count:    107,   Neg. LLF: -239.213597268\n",
      "Iteration:      5,   Func. Count:    133,   Neg. LLF: -239.727352484\n",
      "Iteration:      6,   Func. Count:    158,   Neg. LLF: -241.907188423\n",
      "Iteration:      7,   Func. Count:    183,   Neg. LLF: -245.362215313\n",
      "Iteration:      8,   Func. Count:    209,   Neg. LLF: -246.478708982\n",
      "Iteration:      9,   Func. Count:    236,   Neg. LLF: -246.729412765\n",
      "Iteration:     10,   Func. Count:    262,   Neg. LLF: -246.848960921\n",
      "Iteration:     11,   Func. Count:    288,   Neg. LLF: -247.131319672\n",
      "Iteration:     12,   Func. Count:    313,   Neg. LLF: -248.003884498\n",
      "Iteration:     13,   Func. Count:    338,   Neg. LLF: -248.531255524\n",
      "Iteration:     14,   Func. Count:    364,   Neg. LLF: -248.664429881\n",
      "Iteration:     15,   Func. Count:    390,   Neg. LLF: -248.82346318\n",
      "Iteration:     16,   Func. Count:    416,   Neg. LLF: -248.894270952\n",
      "Iteration:     17,   Func. Count:    442,   Neg. LLF: -248.989449051\n",
      "Iteration:     18,   Func. Count:    468,   Neg. LLF: -249.233211667\n",
      "Iteration:     19,   Func. Count:    493,   Neg. LLF: -250.008390823\n",
      "Iteration:     20,   Func. Count:    519,   Neg. LLF: -250.046338474\n",
      "Iteration:     21,   Func. Count:    544,   Neg. LLF: -250.29755998\n",
      "Iteration:     22,   Func. Count:    568,   Neg. LLF: -251.122013769\n",
      "Iteration:     23,   Func. Count:    594,   Neg. LLF: -251.167736594\n",
      "Iteration:     24,   Func. Count:    619,   Neg. LLF: -252.172862587\n",
      "Iteration:     25,   Func. Count:    645,   Neg. LLF: -252.276435452\n",
      "Iteration:     26,   Func. Count:    671,   Neg. LLF: -252.28273888\n",
      "Iteration:     27,   Func. Count:    696,   Neg. LLF: -252.343338268\n",
      "Iteration:     28,   Func. Count:    721,   Neg. LLF: -252.365169623\n",
      "Iteration:     29,   Func. Count:    745,   Neg. LLF: -252.408371475\n",
      "Iteration:     30,   Func. Count:    769,   Neg. LLF: -252.411918522\n",
      "Iteration:     31,   Func. Count:    793,   Neg. LLF: -252.412777642\n",
      "Iteration:     32,   Func. Count:    817,   Neg. LLF: -252.412958764\n",
      "Iteration:     33,   Func. Count:    841,   Neg. LLF: -252.412996346\n",
      "Iteration:     34,   Func. Count:    865,   Neg. LLF: -252.413009667\n",
      "Iteration:     35,   Func. Count:    889,   Neg. LLF: -252.413011481\n",
      "Optimization terminated successfully.    (Exit mode 0)\n",
      "            Current function value: -252.413012001\n",
      "            Iterations: 35\n",
      "            Function evaluations: 890\n",
      "            Gradient evaluations: 35\n"
     ]
    }
   ],
   "source": [
    "train_G = data[:-10]\n",
    "test_G = data[-10:]\n",
    "am_G = arch.arch_model(train,mean='AR',lags=13,vol='GARCH', p=7) \n",
    "res_G = am.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>AR - ARCH Model Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>        <td>close</td>       <th>  R-squared:         </th>  <td>   0.150</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Mean Model:</th>            <td>AR</td>         <th>  Adj. R-squared:    </th>  <td>   0.096</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Vol Model:</th>            <td>ARCH</td>        <th>  Log-Likelihood:    </th> <td>   252.413</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Distribution:</th>        <td>Normal</td>       <th>  AIC:               </th> <td>  -460.826</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>        <td>Maximum Likelihood</td> <th>  BIC:               </th> <td>  -386.166</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                        <td></td>          <th>  No. Observations:  </th>     <td>220</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>           <td>Sat, Jun 24 2017</td>  <th>  Df Residuals:      </th>     <td>198</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>               <td>12:01:40</td>      <th>  Df Model:          </th>     <td>22</td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Mean Model</caption>\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>      <th>std err</th>       <th>t</th>       <th>P>|t|</th>      <th>95.0% Conf. Int.</th>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Const</th>     <td>1.4927e-03</td>  <td>1.496e-02</td>  <td>9.975e-02</td> <td>    0.921</td> <td>[-2.784e-02,3.082e-02]</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>close[1]</th>  <td>   -0.0850</td>  <td>    0.123</td>  <td>   -0.690</td> <td>    0.490</td>    <td>[ -0.326,  0.156]</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>close[2]</th>  <td>    0.0340</td>  <td>    0.131</td>  <td>    0.260</td> <td>    0.795</td>    <td>[ -0.222,  0.290]</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>close[3]</th>  <td>-4.5421e-04</td> <td>6.748e-02</td> <td>-6.732e-03</td> <td>    0.995</td>    <td>[ -0.133,  0.132]</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>close[4]</th>  <td>   -0.0406</td>  <td>    0.140</td>  <td>   -0.291</td> <td>    0.771</td>    <td>[ -0.314,  0.233]</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>close[5]</th>  <td>   -0.1139</td>  <td>    0.199</td>  <td>   -0.572</td> <td>    0.567</td>    <td>[ -0.504,  0.276]</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>close[6]</th>  <td>   -0.0561</td>  <td>    0.296</td>  <td>   -0.190</td> <td>    0.850</td>    <td>[ -0.636,  0.524]</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>close[7]</th>  <td>-8.9847e-03</td> <td>    0.521</td> <td>-1.724e-02</td> <td>    0.986</td>    <td>[ -1.031,  1.013]</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>close[8]</th>  <td>    0.0203</td>  <td>8.903e-02</td>  <td>    0.228</td> <td>    0.819</td>    <td>[ -0.154,  0.195]</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>close[9]</th>  <td>    0.1843</td>  <td>    0.279</td>  <td>    0.661</td> <td>    0.509</td>    <td>[ -0.362,  0.731]</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>close[10]</th> <td>    0.0917</td>  <td>    0.107</td>  <td>    0.861</td> <td>    0.389</td>    <td>[ -0.117,  0.301]</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>close[11]</th> <td>    0.0745</td>  <td>    0.200</td>  <td>    0.373</td> <td>    0.709</td>    <td>[ -0.317,  0.466]</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>close[12]</th> <td>   -0.0221</td>  <td>    0.136</td>  <td>   -0.162</td> <td>    0.871</td>    <td>[ -0.289,  0.245]</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>close[13]</th> <td>    0.2135</td>  <td>    0.159</td>  <td>    1.340</td> <td>    0.180</td>  <td>[-9.885e-02,  0.526]</td> \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Volatility Model</caption>\n",
       "<tr>\n",
       "      <td></td>        <th>coef</th>     <th>std err</th>      <th>t</th>       <th>P>|t|</th>      <th>95.0% Conf. Int.</th>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>omega</th>    <td>2.9941e-03</td> <td>4.416e-03</td> <td>    0.678</td> <td>    0.498</td> <td>[-5.662e-03,1.165e-02]</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>alpha[1]</th>   <td>0.0000</td>   <td>    0.167</td>   <td>0.000</td>   <td>    1.000</td>    <td>[ -0.328,  0.328]</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>alpha[2]</th> <td>    0.0445</td> <td>6.753e-02</td> <td>    0.659</td> <td>    0.510</td>  <td>[-8.783e-02,  0.177]</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>alpha[3]</th> <td>1.3422e-10</td> <td>    0.404</td> <td>3.319e-10</td> <td>    1.000</td>    <td>[ -0.793,  0.793]</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>alpha[4]</th>   <td>0.0000</td>   <td>    1.217</td>   <td>0.000</td>   <td>    1.000</td>    <td>[ -2.385,  2.385]</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>alpha[5]</th>   <td>0.0000</td>   <td>    0.510</td>   <td>0.000</td>   <td>    1.000</td>    <td>[ -1.000,  1.000]</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>alpha[6]</th> <td>    0.0173</td> <td>    0.674</td> <td>2.560e-02</td> <td>    0.980</td>    <td>[ -1.305,  1.339]</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>alpha[7]</th> <td>    0.5781</td> <td>    1.489</td> <td>    0.388</td> <td>    0.698</td>    <td>[ -2.340,  3.496]</td>  \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                           AR - ARCH Model Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                  close   R-squared:                       0.150\n",
       "Mean Model:                        AR   Adj. R-squared:                  0.096\n",
       "Vol Model:                       ARCH   Log-Likelihood:                252.413\n",
       "Distribution:                  Normal   AIC:                          -460.826\n",
       "Method:            Maximum Likelihood   BIC:                          -386.166\n",
       "                                        No. Observations:                  220\n",
       "Date:                Sat, Jun 24 2017   Df Residuals:                      198\n",
       "Time:                        12:01:40   Df Model:                           22\n",
       "                                  Mean Model                                  \n",
       "==============================================================================\n",
       "                  coef    std err          t      P>|t|       95.0% Conf. Int.\n",
       "------------------------------------------------------------------------------\n",
       "Const       1.4927e-03  1.496e-02  9.975e-02      0.921 [-2.784e-02,3.082e-02]\n",
       "close[1]       -0.0850      0.123     -0.690      0.490      [ -0.326,  0.156]\n",
       "close[2]        0.0340      0.131      0.260      0.795      [ -0.222,  0.290]\n",
       "close[3]   -4.5421e-04  6.748e-02 -6.732e-03      0.995      [ -0.133,  0.132]\n",
       "close[4]       -0.0406      0.140     -0.291      0.771      [ -0.314,  0.233]\n",
       "close[5]       -0.1139      0.199     -0.572      0.567      [ -0.504,  0.276]\n",
       "close[6]       -0.0561      0.296     -0.190      0.850      [ -0.636,  0.524]\n",
       "close[7]   -8.9847e-03      0.521 -1.724e-02      0.986      [ -1.031,  1.013]\n",
       "close[8]        0.0203  8.903e-02      0.228      0.819      [ -0.154,  0.195]\n",
       "close[9]        0.1843      0.279      0.661      0.509      [ -0.362,  0.731]\n",
       "close[10]       0.0917      0.107      0.861      0.389      [ -0.117,  0.301]\n",
       "close[11]       0.0745      0.200      0.373      0.709      [ -0.317,  0.466]\n",
       "close[12]      -0.0221      0.136     -0.162      0.871      [ -0.289,  0.245]\n",
       "close[13]       0.2135      0.159      1.340      0.180   [-9.885e-02,  0.526]\n",
       "                               Volatility Model                              \n",
       "=============================================================================\n",
       "                 coef    std err          t      P>|t|       95.0% Conf. Int.\n",
       "-----------------------------------------------------------------------------\n",
       "omega      2.9941e-03  4.416e-03      0.678      0.498 [-5.662e-03,1.165e-02]\n",
       "alpha[1]       0.0000      0.167      0.000      1.000      [ -0.328,  0.328]\n",
       "alpha[2]       0.0445  6.753e-02      0.659      0.510   [-8.783e-02,  0.177]\n",
       "alpha[3]   1.3422e-10      0.404  3.319e-10      1.000      [ -0.793,  0.793]\n",
       "alpha[4]       0.0000      1.217      0.000      1.000      [ -2.385,  2.385]\n",
       "alpha[5]       0.0000      0.510      0.000      1.000      [ -1.000,  1.000]\n",
       "alpha[6]       0.0173      0.674  2.560e-02      0.980      [ -1.305,  1.339]\n",
       "alpha[7]       0.5781      1.489      0.388      0.698      [ -2.340,  3.496]\n",
       "=============================================================================\n",
       "\n",
       "Covariance estimator: robust\n",
       "\"\"\""
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_G.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Const        1.492690e-03\n",
       "close[1]    -8.497512e-02\n",
       "close[2]     3.395028e-02\n",
       "close[3]    -4.542113e-04\n",
       "close[4]    -4.056921e-02\n",
       "close[5]    -1.138923e-01\n",
       "close[6]    -5.611123e-02\n",
       "close[7]    -8.984683e-03\n",
       "close[8]     2.034156e-02\n",
       "close[9]     1.843336e-01\n",
       "close[10]    9.172263e-02\n",
       "close[11]    7.449631e-02\n",
       "close[12]   -2.210782e-02\n",
       "close[13]    2.135335e-01\n",
       "omega        2.994095e-03\n",
       "alpha[1]     0.000000e+00\n",
       "alpha[2]     4.451640e-02\n",
       "alpha[3]     1.342153e-10\n",
       "alpha[4]     0.000000e+00\n",
       "alpha[5]     0.000000e+00\n",
       "alpha[6]     1.726823e-02\n",
       "alpha[7]     5.781235e-01\n",
       "Name: params, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_G.params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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WjBiFbpnIdRBjup5sBMX+nlBGhocd4oEVN3gknsKh/rBhKQU8zoxMDvnBtQoR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lIYhj4kdwO/1z/bgIFRrrkamA0I4H3gTH17HjAFeKGjD0kv60eyaZ9vAR/08f/lmtBz9tW+hOxz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\n7bw6wH/sAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x80913320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res_G.plot()\n",
    "plt.plot(data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.01362123]\n",
      "[ 0.00395096]\n"
     ]
    }
   ],
   "source": [
    "forecasts = res_G.forecast()\n",
    "print forecasts.mean[-1:].values[0]\n",
    "print forecasts.variance[-1:].values[0]"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
